English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

The Conformer model is an excellent architecture for speech recognitionmodeling that effectively utilizes the hybrid losses of connectionist temporalclassification (CTC) and attention to train model parameters. To improve thedecoding efficiency of Conformer, we propose a novel connectionist temporalsummarization (CTS) method that reduces the number of frames required for theattention decoder fed from the acoustic sequences generated by the encoder,thus reducing operations. However, to achieve such decoding improvements, wemust fine-tune model parameters, as cross-attention observations are changedand thus require corresponding refinements. Our final experiments show that,with a beamwidth of 4, the LibriSpeech's decoding budget can be reduced by upto 20% and for FluentSpeech data it can be reduced by 11%, without losing ASRaccuracy. An improvement in accuracy is even found for the LibriSpeech"test-other" set. The word error rate (WER) is reduced by 6\% relative at thebeam width of 1 and by 3% relative at the beam width of 4.

Adding Connectionist Temporal Summarization into Conformer to Improve Its Decoder Efficiency For Speech Recognition